In [1]:
import pandas as pd
In [3]:
data_assign = pd.read_csv("Car Price.csv")
In [5]:
data_assign
Out[5]:
Brand Model Year Selling_Price KM_Driven Fuel Seller_Type Transmission Owner
0 Maruti Maruti 800 AC 2007 60000 70000 Petrol Individual Manual First Owner
1 Maruti Maruti Wagon R LXI Minor 2007 135000 50000 Petrol Individual Manual First Owner
2 Hyundai Hyundai Verna 1.6 SX 2012 600000 100000 Diesel Individual Manual First Owner
3 Datsun Datsun RediGO T Option 2017 250000 46000 Petrol Individual Manual First Owner
4 Honda Honda Amaze VX i-DTEC 2014 450000 141000 Diesel Individual Manual Second Owner
... ... ... ... ... ... ... ... ... ...
4335 Hyundai Hyundai i20 Magna 1.4 CRDi (Diesel) 2014 409999 80000 Diesel Individual Manual Second Owner
4336 Hyundai Hyundai i20 Magna 1.4 CRDi 2014 409999 80000 Diesel Individual Manual Second Owner
4337 Maruti Maruti 800 AC BSIII 2009 110000 83000 Petrol Individual Manual Second Owner
4338 Hyundai Hyundai Creta 1.6 CRDi SX Option 2016 865000 90000 Diesel Individual Manual First Owner
4339 Renault Renault KWID RXT 2016 225000 40000 Petrol Individual Manual First Owner

4340 rows × 9 columns

In [7]:
data_assign.head()
Out[7]:
Brand Model Year Selling_Price KM_Driven Fuel Seller_Type Transmission Owner
0 Maruti Maruti 800 AC 2007 60000 70000 Petrol Individual Manual First Owner
1 Maruti Maruti Wagon R LXI Minor 2007 135000 50000 Petrol Individual Manual First Owner
2 Hyundai Hyundai Verna 1.6 SX 2012 600000 100000 Diesel Individual Manual First Owner
3 Datsun Datsun RediGO T Option 2017 250000 46000 Petrol Individual Manual First Owner
4 Honda Honda Amaze VX i-DTEC 2014 450000 141000 Diesel Individual Manual Second Owner
In [9]:
data_assign.describe()
Out[9]:
Year Selling_Price KM_Driven
count 4340.000000 4.340000e+03 4340.000000
mean 2013.090783 5.041273e+05 66215.777419
std 4.215344 5.785487e+05 46644.102194
min 1992.000000 2.000000e+04 1.000000
25% 2011.000000 2.087498e+05 35000.000000
50% 2014.000000 3.500000e+05 60000.000000
75% 2016.000000 6.000000e+05 90000.000000
max 2020.000000 8.900000e+06 806599.000000
In [11]:
data_assign.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4340 entries, 0 to 4339
Data columns (total 9 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Brand          4340 non-null   object
 1   Model          4340 non-null   object
 2   Year           4340 non-null   int64 
 3   Selling_Price  4340 non-null   int64 
 4   KM_Driven      4340 non-null   int64 
 5   Fuel           4340 non-null   object
 6   Seller_Type    4340 non-null   object
 7   Transmission   4340 non-null   object
 8   Owner          4340 non-null   object
dtypes: int64(3), object(6)
memory usage: 305.3+ KB
In [13]:
data_assign.columns
Out[13]:
Index(['Brand', 'Model', 'Year', 'Selling_Price', 'KM_Driven', 'Fuel',
       'Seller_Type', 'Transmission', 'Owner'],
      dtype='object')
In [15]:
data_assign.columns.tolist()
Out[15]:
['Brand',
 'Model',
 'Year',
 'Selling_Price',
 'KM_Driven',
 'Fuel',
 'Seller_Type',
 'Transmission',
 'Owner']
In [17]:
Unique_data = {}

for column in data_assign.columns: 
    Unique_data[column] = data_assign[column].nunique()
In [19]:
Unique_data
Out[19]:
{'Brand': 29,
 'Model': 1491,
 'Year': 27,
 'Selling_Price': 445,
 'KM_Driven': 770,
 'Fuel': 5,
 'Seller_Type': 3,
 'Transmission': 2,
 'Owner': 5}
In [21]:
import matplotlib.pyplot as plt
In [23]:
fuel_counts = data_assign['Fuel'].value_counts()


plt.bar(fuel_counts.keys(), fuel_counts.values)
plt.title("Fuel Type Distribution")
plt.xlabel("Fuel Type")
plt.ylabel("Number of Cars")

plt.show()
No description has been provided for this image
In [25]:
seller_counts = data_assign['Seller_Type'].value_counts()

plt.pie(seller_counts.values,labels=seller_counts.index)
plt.title("Cars by Seller Type")
plt.figure()
plt.show()
No description has been provided for this image 
<Figure size 640x480 with 0 Axes>
In [27]:
plt.figure(figsize=(12,6))
plt.hist(data_assign['KM_Driven'], color='green', edgecolor='black')
plt.title("Distribution of KM_Driven")
plt.xlabel("KM Driven")
plt.ylabel("Number of Cars")
plt.grid(axis='y')
plt.show()
No description has been provided for this image
In [29]:
owner_counts = data_assign['Owner'].value_counts()

plt.pie(owner_counts.values,labels=owner_counts.index)
plt.title("Distributed of Cars based on their Ownership Status")
plt.figure()
plt.show()
No description has been provided for this image 
<Figure size 640x480 with 0 Axes>

Linear Regressions

In [31]:
X = data_assign[['KM_Driven']] 
y = data_assign['Selling_Price']
In [33]:
from sklearn.model_selection import train_test_split
In [35]:
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42)
In [37]:
from sklearn.linear_model import LinearRegression
In [39]:
model = LinearRegression()
model
Out[39]:
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
In [41]:
model.fit(X_train, y_train)
Out[41]:
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LinearRegression()
In [43]:
lin_pred = model.predict(X_test)
In [45]:
lin_pred
Out[45]:
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        380093.81043884,  569289.49325501,  648562.48435499,
        592938.95360703])
In [47]:
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
In [49]:
mean_absolute_error(y_test, lin_pred)
Out[49]:
300629.95639712235
In [51]:
mean_squared_error(y_test, lin_pred)
Out[51]:
292829552069.2159
In [53]:
r2_score(y_test, lin_pred)
Out[53]:
0.04043884538588105
In [55]:
import matplotlib.pyplot as plt
In [57]:
plt.figure()
plt.hist(data_assign['KM_Driven'])
plt.xlabel("KM Driven")
plt.ylabel("Number of Cars")
plt.title("Distribution of KM Driven Values")
plt.show()
No description has been provided for this image
In [59]:
plt.pie([y_test.sum(), lin_pred.sum()],labels=["Actual Selling Price", "Predicted Selling Price"])
plt.title("Actual vs Predicted Selling Price")
plt.figure()
plt.show()
No description has been provided for this image 
<Figure size 640x480 with 0 Axes>
In [61]:
actual_avg = y_test.mean()
predicted_avg = lin_pred.mean()

plt.figure()
plt.bar(["Actual Price", "Predicted Price"], [actual_avg, predicted_avg])
plt.xlabel("Price Type")
plt.ylabel("Average Selling Price")
plt.title("Comparison of Average Actual and Predicted Selling Prices")
plt.show()
No description has been provided for this image

Decision Tree Regressions

In [63]:
from sklearn.preprocessing import LabelEncoder
In [65]:
fuel_encoder = LabelEncoder()
data_assign["Fuel_Encoded"] = fuel_encoder.fit_transform(data_assign["Fuel"])
In [67]:
X = data_assign[['Fuel_Encoded']]
y = data_assign['Selling_Price']
In [69]:
from sklearn.model_selection import train_test_split
In [71]:
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.2, random_state=42)
In [73]:
from sklearn.tree import DecisionTreeClassifier
In [75]:
model_tree = DecisionTreeClassifier()
In [77]:
model_tree
Out[77]:
DecisionTreeClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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DecisionTreeClassifier()
In [79]:
model_tree.fit(X_train, y_train)
Out[79]:
DecisionTreeClassifier()
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DecisionTreeClassifier()
In [81]:
y_pred = model_tree.predict(X_test)
In [83]:
y_pred
Out[83]:
array([450000, 300000, 300000, 450000, 450000, 300000, 300000, 300000,
       450000, 300000, 450000, 450000, 450000, 450000, 300000, 300000,
       450000, 450000, 300000, 450000, 450000, 450000, 300000, 450000,
       450000, 450000, 450000, 300000, 300000, 300000, 300000, 300000,
       450000, 450000, 300000, 450000, 450000, 300000, 300000, 300000,
       450000, 300000, 450000, 450000, 450000, 450000, 300000, 450000,
       450000, 450000, 450000, 300000, 300000, 300000, 300000, 450000,
       450000, 450000, 300000, 300000, 300000, 450000, 300000, 450000,
       300000, 300000, 300000, 300000, 450000, 450000, 450000, 300000,
       300000, 450000, 450000, 450000, 450000, 450000, 300000, 450000,
       450000, 300000, 300000, 300000, 300000, 450000, 450000, 300000,
       300000, 300000, 300000, 300000, 300000, 300000, 300000, 450000,
       450000, 450000, 300000, 450000, 300000, 450000, 450000, 300000,
       450000, 450000, 300000, 450000, 300000, 450000,  80000, 300000,
       450000, 300000, 300000, 450000,  80000, 300000, 450000, 450000,
       450000, 450000, 450000, 450000, 450000, 300000, 300000, 450000,
       300000, 300000, 450000, 450000, 450000, 300000, 300000, 450000,
       450000, 300000, 300000, 300000, 300000, 300000, 450000, 300000,
       450000, 300000, 450000, 450000, 450000, 450000, 300000, 365000,
       450000, 300000, 450000, 300000, 450000, 300000, 300000, 450000,
       450000, 450000, 450000, 300000, 300000, 450000, 450000, 450000,
       300000, 300000, 450000, 300000, 300000, 300000, 450000, 450000,
       300000, 450000, 300000, 450000, 450000, 450000, 450000, 300000,
       300000, 450000, 300000, 450000, 300000, 450000, 450000, 300000,
       450000, 300000, 300000, 300000, 450000, 300000, 450000, 300000,
       300000, 450000, 450000, 450000, 300000, 450000, 300000, 300000,
       300000, 450000, 300000, 450000, 450000, 300000, 300000, 300000,
       300000, 450000, 300000, 300000, 450000, 300000, 300000, 300000,
       450000, 450000, 450000, 450000, 450000, 450000, 300000, 300000,
       450000, 300000, 300000, 450000, 300000, 300000, 450000, 450000,
       450000, 300000, 450000, 300000, 300000, 450000, 450000, 300000,
       450000, 300000, 300000, 450000, 300000, 300000, 450000, 300000,
       450000, 300000, 300000, 300000, 450000, 300000, 300000, 300000,
       450000, 450000, 450000, 300000, 300000, 300000, 450000, 450000,
       300000, 300000, 450000, 450000, 450000, 450000, 450000, 450000,
       300000, 450000, 450000, 450000, 300000, 300000, 450000, 450000,
       300000, 300000, 300000, 300000, 300000, 300000, 300000, 450000,
       300000, 300000, 300000, 450000, 450000, 300000, 300000, 450000,
       300000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
       300000, 450000, 450000, 300000, 450000, 450000, 450000, 300000,
       450000, 450000, 450000, 300000, 300000, 300000, 450000, 450000,
       450000, 300000, 450000, 450000, 450000, 450000, 300000, 300000,
       300000, 450000, 300000, 300000, 450000, 300000, 300000, 450000,
       300000, 450000, 450000, 450000, 300000, 300000, 300000, 450000,
       450000, 300000, 300000, 450000, 450000, 450000, 450000, 300000,
       300000, 450000, 450000, 300000, 300000, 300000, 300000, 450000,
       450000, 450000, 450000, 450000, 300000, 450000, 450000, 300000,
       450000, 450000, 450000, 300000, 450000, 450000, 300000, 450000,
       300000, 450000, 450000, 450000, 450000, 450000, 450000, 300000,
       450000, 450000, 300000, 450000, 450000, 300000, 300000, 450000,
       450000, 300000, 450000, 450000, 450000, 300000, 300000, 450000,
       450000, 300000, 450000, 450000, 450000, 365000, 450000, 300000,
       300000, 300000, 450000, 450000, 450000, 300000, 300000, 300000,
       300000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
       300000, 300000, 450000, 300000, 450000, 450000, 450000, 450000,
       300000, 450000, 300000, 450000, 300000, 300000, 300000, 450000,
       450000, 450000, 450000, 300000, 300000, 450000, 300000, 300000,
       300000, 450000, 450000, 300000, 450000, 300000, 450000, 300000,
       300000, 450000, 300000, 450000, 450000, 300000, 450000, 450000,
        80000, 450000, 300000, 450000, 300000, 450000, 300000, 450000,
        80000, 450000, 300000, 450000, 300000, 300000, 300000, 450000,
       300000, 300000, 450000, 300000, 300000, 300000, 450000, 450000,
       300000, 450000, 450000, 450000, 300000, 300000, 450000, 450000,
       365000, 450000, 450000, 450000, 450000, 300000, 450000, 300000,
       300000, 450000, 300000, 300000, 450000, 300000, 450000, 450000,
       450000, 300000, 450000, 300000, 450000, 450000, 450000, 300000,
       365000, 300000, 300000, 450000, 300000, 450000, 450000, 300000,
       450000, 300000, 450000, 300000, 450000, 450000, 450000, 450000,
       450000, 450000, 300000, 450000, 300000, 450000, 300000, 300000,
       300000, 450000, 450000, 300000, 300000, 450000, 450000, 300000,
       450000, 450000, 300000, 300000, 450000, 300000, 450000, 300000,
       300000, 300000, 450000, 300000, 450000, 300000, 450000, 450000,
       300000, 450000, 300000, 300000, 300000, 450000, 450000, 450000,
       450000, 300000, 300000, 450000, 450000, 450000, 450000, 450000,
       300000, 300000, 300000, 300000, 300000, 300000, 300000, 450000,
       300000, 300000, 300000, 450000, 450000, 300000, 450000, 300000,
       300000, 300000, 300000, 300000, 450000, 300000, 450000, 450000,
       450000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
       300000, 450000, 300000, 300000, 300000, 450000, 450000, 450000,
       450000, 300000, 450000, 365000, 450000,  80000, 450000, 300000,
       450000, 300000, 300000, 450000, 300000, 450000, 300000, 300000,
       450000, 300000, 300000, 450000, 450000, 300000, 300000, 300000,
       300000, 450000, 450000, 450000, 450000, 300000, 300000, 450000,
       300000, 300000, 300000, 450000, 300000, 300000, 300000, 300000,
       300000, 300000, 300000, 300000, 300000, 450000, 300000, 300000,
       300000, 450000, 300000, 300000, 300000, 450000, 300000, 450000,
       300000, 300000, 450000, 450000, 450000, 450000, 300000, 450000,
       450000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
       300000, 450000, 300000, 450000, 300000, 450000, 450000, 450000,
       300000, 450000, 300000, 300000, 450000, 450000, 450000, 450000,
       450000, 300000, 450000, 450000, 450000, 300000, 300000, 300000,
       300000, 450000, 300000, 450000, 450000, 450000, 300000, 450000,
       300000, 300000, 450000, 300000, 450000, 300000, 300000, 450000,
       300000, 300000, 300000, 450000, 300000, 300000, 450000, 450000,
       300000, 300000, 450000, 300000, 450000, 300000, 300000, 450000,
       450000, 450000, 450000, 450000, 300000, 450000, 450000, 300000,
       300000, 300000, 300000, 300000, 450000, 450000, 300000, 300000,
       300000, 450000, 450000, 300000, 450000, 300000, 300000, 300000,
       300000, 300000, 300000, 300000, 450000, 300000, 300000, 450000,
       300000, 450000, 300000, 450000, 300000, 300000, 300000, 300000,
       450000, 450000, 300000, 300000, 450000, 300000, 300000, 450000,
       300000, 300000, 450000, 450000, 450000, 450000, 300000, 300000,
       450000, 300000, 300000, 450000, 450000, 450000, 300000, 300000,
       450000, 450000, 450000, 450000, 300000, 450000, 450000, 450000,
       300000, 300000, 450000, 450000, 300000, 450000, 450000, 300000,
       300000, 300000, 300000, 450000, 450000, 300000, 450000, 300000,
       450000, 450000, 300000, 300000, 450000, 450000, 300000, 300000,
       450000, 300000, 450000, 300000, 450000, 450000, 300000, 300000,
       450000, 450000, 300000, 300000], dtype=int64)
In [85]:
mean_absolute_error(y_test, y_pred)
Out[85]:
273683.28686635947
In [87]:
mean_squared_error(y_test, y_pred)
Out[87]:
302642379882.89056
In [89]:
r2_score(y_test, y_pred)
Out[89]:
0.008283592200596268
In [91]:
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
In [93]:
plot_tree(model_tree);
No description has been provided for this image
In [95]:
plt.figure(figsize=(40,20), dpi=800)
plot_tree(
    model_tree,
    feature_names=["Fuel_Encoded"],
    filled=True,
    rounded=True,
    fontsize=9,
    max_depth=2
)
plt.savefig('Course_work_image')
plt.show()
No description has been provided for this image
In [97]:
plt.figure(figsize=(40,20), dpi=800)
plot_tree(
    model_tree,
    feature_names=["Fuel_Encoded"],
    filled=True,
    rounded=True,
    fontsize=9,
    max_depth=1
)

plt.show()
No description has been provided for this image

Random Forest Regression

In [99]:
from sklearn.preprocessing import LabelEncoder
In [101]:
owner_encoder = LabelEncoder()
data_assign["Owner_Encoded"] = owner_encoder.fit_transform(data_assign["Owner"])
In [103]:
X = data_assign[['Owner_Encoded']]
y = data_assign['Selling_Price']
In [105]:
from sklearn.model_selection import train_test_split
In [107]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [109]:
from sklearn.ensemble import RandomForestRegressor
In [111]:
model_forest = RandomForestRegressor()
In [113]:
model_forest
Out[113]:
RandomForestRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestRegressor()
In [115]:
model_forest.fit(X_train, y_train)
Out[115]:
RandomForestRegressor()
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RandomForestRegressor()
In [117]:
pred_for = model_forest.predict(X_test)
In [119]:
pred_for
Out[119]:
array([603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       342845.64268155, 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       271658.49708117, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       271658.49708117, 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       271658.49708117, 271658.49708117, 603825.561941  , 271658.49708117,
       603825.561941  , 342845.64268155, 271658.49708117, 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 178373.5848655 , 342845.64268155, 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 342845.64268155, 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 271658.49708117, 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 271658.49708117, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 271658.49708117, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       271658.49708117, 342845.64268155, 342845.64268155, 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 178373.5848655 , 342845.64268155, 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 271658.49708117, 603825.561941  , 342845.64268155,
       342845.64268155, 342845.64268155, 271658.49708117, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       271658.49708117, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 342845.64268155, 271658.49708117,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 908553.8992969 ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       271658.49708117, 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 271658.49708117, 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 342845.64268155, 271658.49708117,
       342845.64268155, 603825.561941  , 603825.561941  , 342845.64268155,
       342845.64268155, 342845.64268155, 342845.64268155, 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 271658.49708117, 342845.64268155, 603825.561941  ,
       603825.561941  , 271658.49708117, 271658.49708117, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       178373.5848655 , 342845.64268155, 342845.64268155, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 271658.49708117,
       603825.561941  , 603825.561941  , 603825.561941  , 271658.49708117,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       342845.64268155, 342845.64268155, 342845.64268155, 342845.64268155,
       271658.49708117, 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 271658.49708117, 342845.64268155, 603825.561941  ,
       603825.561941  , 342845.64268155, 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 271658.49708117, 603825.561941  ,
       342845.64268155, 271658.49708117, 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       271658.49708117, 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 271658.49708117, 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 271658.49708117, 271658.49708117,
       342845.64268155, 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 271658.49708117, 342845.64268155, 603825.561941  ,
       603825.561941  , 271658.49708117, 271658.49708117, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       271658.49708117, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 271658.49708117, 342845.64268155,
       342845.64268155, 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 342845.64268155,
       342845.64268155, 271658.49708117, 603825.561941  , 271658.49708117,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 271658.49708117, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 342845.64268155,
       342845.64268155, 603825.561941  , 603825.561941  , 271658.49708117,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 271658.49708117, 342845.64268155, 603825.561941  ,
       271658.49708117, 603825.561941  , 603825.561941  , 271658.49708117,
       271658.49708117, 603825.561941  , 603825.561941  , 271658.49708117,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 271658.49708117, 603825.561941  , 603825.561941  ,
       603825.561941  , 271658.49708117, 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 271658.49708117,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 178373.5848655 , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 271658.49708117,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 271658.49708117, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       271658.49708117, 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 908553.8992969 ,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       342845.64268155, 271658.49708117, 603825.561941  , 603825.561941  ,
       342845.64268155, 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 342845.64268155, 342845.64268155, 342845.64268155,
       603825.561941  , 603825.561941  , 342845.64268155, 342845.64268155,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       342845.64268155, 342845.64268155, 178373.5848655 , 603825.561941  ,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 271658.49708117, 342845.64268155,
       342845.64268155, 603825.561941  , 342845.64268155, 603825.561941  ,
       342845.64268155, 342845.64268155, 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 342845.64268155,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 603825.561941  ,
       178373.5848655 , 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 271658.49708117,
       603825.561941  , 342845.64268155, 342845.64268155, 603825.561941  ,
       271658.49708117, 603825.561941  , 178373.5848655 , 271658.49708117,
       342845.64268155, 603825.561941  , 603825.561941  , 603825.561941  ,
       603825.561941  , 603825.561941  , 342845.64268155, 178373.5848655 ,
       603825.561941  , 603825.561941  , 271658.49708117, 603825.561941  ,
       603825.561941  , 271658.49708117, 603825.561941  , 271658.49708117,
       603825.561941  , 178373.5848655 , 603825.561941  , 271658.49708117,
       603825.561941  , 342845.64268155, 603825.561941  , 603825.561941  ])
In [121]:
mean_absolute_error(y_test, pred_for)
Out[121]:
293387.64333832584
In [123]:
mean_squared_error(y_test, pred_for)
Out[123]:
289393358096.79443
In [125]:
r2_score(y_test, pred_for)
Out[125]:
0.05169876854717259
In [127]:
import matplotlib.pyplot as plt
In [129]:
plt.figure(figsize=(6,4))
plt.scatter(X_test["Owner_Encoded"], y_test, color='blue', label='Actual Price')
plt.scatter(X_test["Owner_Encoded"], pred_for, color='red', label='Predicted Price')
plt.title('Random Forest Regression: Owner & Selling_Price')
plt.xlabel('Owner (Encoded)')
plt.ylabel('Selling_Price')
plt.show()
No description has been provided for this image
In [131]:
plt.figure(figsize=(6,6))
plt.pie([y_test.sum(), pred_for.sum()],labels=['Actual Selling Price', 'Predicted Selling Price'],startangle=90)
plt.title('Actual vs Predicted Selling Price')
plt.show()
No description has been provided for this image
In [133]:
plt.figure(figsize=(8,4))
plt.plot(y_test.values, label='Actual Price')
plt.plot(pred_for, label='Predicted Price')
plt.title('Actual vs Predicted Selling Price')
plt.xlabel('Test Data Index')
plt.ylabel('Selling Price')
plt.legend()
plt.show()
No description has been provided for this image
In [135]:
plt.figure(figsize=(6,4))
plt.hist(y_test, bins=20, label='Actual Price')
plt.hist(pred_for, bins=20, label='Predicted Price')
plt.title('Distribution of Selling Price')
plt.xlabel('Selling Price')
plt.ylabel('Frequency')
plt.legend()
plt.show()
No description has been provided for this image

Comparison between the all Algorithms used.

In [137]:
import pandas as pd
In [139]:
comparison = pd.DataFrame({
    "Actual Selling Price": y_test,
    "Linear_Regression": lin_pred,
    "Decision_Tree": y_pred,
    "Random_Forest": pred_for
})
In [141]:
comparison
Out[141]:
Actual Selling Price Linear_Regression Decision_Tree Random_Forest
3978 165000 474691.651847 450000 603825.561941
1448 250000 600033.791713 300000 603825.561941
2664 120000 565214.691236 300000 342845.642682
17 450000 330429.943700 450000 342845.642682
1634 730000 521990.572551 450000 342845.642682
... ... ... ... ...
3468 130000 521990.572551 300000 271658.497081
3164 350000 380093.810439 450000 603825.561941
416 150000 569289.493255 450000 342845.642682
1616 650000 648562.484355 300000 603825.561941
2480 575000 592938.953607 300000 603825.561941

868 rows × 4 columns

In [143]:
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
In [145]:
metrics_comparison = pd.DataFrame({
    "Linear_Regression": {
        "MAE": mean_absolute_error(y_test, lin_pred),
        "MSE": mean_squared_error(y_test, lin_pred),
        "R2": r2_score(y_test, lin_pred)
    },
    "Decision_Tree": {
        "MAE": mean_absolute_error(y_test, y_pred),
        "MSE": mean_squared_error(y_test, y_pred),
        "R2": r2_score(y_test, y_pred)
    },
    "Random_Forest": {
        "MAE": mean_absolute_error(y_test, pred_for),
        "MSE": mean_squared_error(y_test, pred_for),
        "R2": r2_score(y_test, pred_for)
    }
})
In [147]:
metrics_comparison
Out[147]:
Linear_Regression Decision_Tree Random_Forest
MAE 3.006300e+05 2.736833e+05 2.933876e+05
MSE 2.928296e+11 3.026424e+11 2.893934e+11
R2 4.043885e-02 8.283592e-03 5.169877e-02
In [149]:
plt.figure(figsize=(18,5))


plt.subplot(1, 3, 1)
plt.scatter(y_test, lin_pred)
plt.title("Linear Regression")
plt.xlabel("Actual Selling_Price")
plt.ylabel("Predicted Selling_Price")


plt.subplot(1, 3, 2)
plt.scatter(y_test, y_pred)
plt.title("Decision Tree Regression")
plt.xlabel("Actual Selling_Price")
plt.ylabel("Predicted Selling_Price")


plt.subplot(1, 3, 3)
plt.scatter(y_test, pred_for)
plt.title("Random Forest Regression")
plt.xlabel("Actual Selling_Price")
plt.ylabel("Predicted Selling_Price")

plt.tight_layout()
plt.show()
No description has been provided for this image
In [151]:
plt.figure(figsize=(6,6))
plt.pie(metrics_comparison.loc['MAE'],labels=metrics_comparison.columns,autopct='%1.1f%%',startangle=90)
plt.title('MAE Comparison in Algorithms')
plt.show()
No description has been provided for this image
In [153]:
plt.figure(figsize=(10,5))
plt.pie(metrics_comparison.loc['MSE'],labels=metrics_comparison.columns,autopct='%1.1f%%',startangle=90)
plt.title('MSE Comparison in Algorithms')
plt.show()
No description has been provided for this image
In [155]:
plt.figure(figsize=(10,5))
plt.pie(metrics_comparison.loc['R2'],labels=metrics_comparison.columns,autopct='%1.1f%%',startangle=90)
plt.title('R² Score Comparison in Algorithms')
plt.show()
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